Effective Evaluation of Medical Images Using Artificial Intelligence Techniques

• Published in: Computational intelligence and neuroscience Vol. 2022; pp. 1 - 9
• Main Authors: Kannan, S., Premalatha, G., Jamuna Rani, M., Jayakumar, D., Senthil, P., Palanivelrajan, S., Devi, S., Sahile, Kibebe
• Format: Journal Article
• Language: English
• Published: New York Hindawi 10.08.2022; John Wiley & Sons, Inc; Hindawi Limited
• Subjects: Algorithms; Analysis; Artificial intelligence; Artificial neural networks; Classification; Convulsions & seizures; Data mining; Deep learning; Diagnosis; Drug therapy; EEG; Electroencephalography; Epilepsy; Evaluation; Exports; Learning algorithms; Long short-term memory; Machine learning; Medical imaging; Medical imaging equipment; Medical research; Medicine, Experimental; Methods; Movement disorders; Neural networks; Neurodegenerative diseases; Parkinson's disease; Patients; Predictions; Risk analysis; Risk factors; Seizures; Seizures (Medicine); Signs and symptoms; Support vector machines; Wavelet transforms; India
• ISSN: 1687-5265; 1687-5273
• DOI: 10.1155/2022/8419308

This work is implemented for the management of patients with epilepsy, and methods based on electroencephalography (EEG) analysis have been proposed for the timely prediction of its occurrence. The proposed system is used for crisis detection and prediction system; it is useful for both patients and medical staff to know their status easily and more accurately. In the treatment of Parkinson’s disease, the affected patients with Parkinson’s disease can assess the prognostic risk factors, and the symptoms are evaluated to predict rapid progression in the early stages after diagnosis. The presented seizure prediction system introduces deep learning algorithms into EEG score analysis. This proposed work long short-term memory (LSTM) network model is mainly implemented for the identification and classification of qualitative patterns in the EEG of patients. While compared with other techniques like deep learning models such as convolutional neural networks (CNNs) and traditional machine learning algorithms, the proposed LSTM model plays a significant role in predicting impending crises over 4 different qualifying intervals from 10 minutes to 1.5 hours with very few wrong predictions.